The subject matter disclosed herein relates generally to data reconstruction systems and methods, and more particularly to systems and methods to estimate properties of regions of interest, particularly in soft-field reconstructions of multi-material objects.
Soft-field tomography, such as Electrical Impedance Tomography (EIT), diffuse optical tomography, elastography, and related modalities may be used to measure the internal properties of an object, such as the electrical properties of materials comprising internal structures of the object. For example, in EIT systems, an estimate is made of the distribution of electrical conductivities of the internal structures. Such EIT systems reconstruct the conductivity and/or permittivity of the materials within the area or volume based on an applied excitation (e.g., current) and a measured response (e.g., voltage) acquired at or proximate the surface of the area or volume. Visual distributions of the estimates can then be formed.
In soft-field tomography, conventional reconstruction algorithms can solve for an impedance distribution within the object without using any prior information. However, such reconstruction processes are computationally intensive because of the iterations needed to converge to a solution. Thus, the reconstruction process for these conventional algorithms can be very time consuming and requires high speed electronics and processors. Accordingly, if rapid measurements are needed, such as for visualizing in real-time a multi-material object, such as the flow of gas through a pipe, conventional reconstruction algorithms will not perform satisfactorily. Moreover, conventional reconstruction algorithms cannot accommodate high contrast in the real impedance distribution of an object.